• Journal of the Korean Society of Food Science and Nutrition
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• v.23 no.5
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• pp.774-783
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• 1994

New food item numbers for each food included in Food Composition Table in Korea (4th ed) and other Food Composition Table. New Food item numbers classified to base 5-basic food groups and its classification was as follows. 1 As for the 1569 food items , they were classified as 20 food sub-groups(82 food sorts) for 5-basic food groups. 2. As for the 82 food sorts, they were individually classified with raw prepared , fat sugar content and arranged in order, ㄱ , ㄴ and ㄷ and made the item number. 3. The data set of nutritive value of food with new item numbers was accessed on computer files. 4. The Food & Description Table was drafted as 1572 food items were arranged in order, ㄱ, ㄴand ㄷ. 5. The Food Table arranged in the order or each nutrient content (energy, carbohydrate, protein , etc....) was drafted. Clipper program for computing nutritive values and tabulation of nutrients of daily diet were coded by applying new food item numbers. It is expected that should utilized as a basic data of computer program for calculating the nutritive value of diet, evaluating the nutrition and counseling the nutrition.

• Journal of Nutrition and Health
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• v.20 no.6
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• pp.442-455
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• 1987

The purpose of the study were to assist foodservice managers in complex decision making by utilizing computerized cost accounting system and to relieve managers from repetitive and routine tasks so that more adequate patient care and consultation can be provided. The scope of the computer-assisted cost accounting system consists of budget, menu planning, purchasing, inventory, cost control and financial reporting. The content of the computerized system are summarized as follows ; 1) For budgeting monthly income was estimated by calculating unit cost of each meal and forecasting serving numbers. The actual serving numbers for patients and employees were totaled everyday, and utilized as the basic data base for estimating income and planning menu. The monthly lists of meal sensus were generated. 2) for menu planning concersion factors were computed based on the standarized recipe for 50 servings. Daily menus for patients and employees which include total amounts of each ingredient and cost analyzed information were generated. 3) Daily and monthly purchasing report for each food item classified by patient and employee meals were generated. 4) Inventory transactions such as recipts and issues were totalized daily for each stocked item, and monthly inventory reports were generated. 5) Cost analysis reports for each menu item were generated into two ways based on the budget coat as well as the purchasing cost. 6) Editing new recipes and updating food costs change to the data base were carried out. 7) Financial reports were generated monthly, first-half and second-half of the year, and yearly basis.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.81-89
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• 2020

Online reviews we encounter commonly on SNS, although a complex range of assessment information affecting the consumer's preferences are included, it is general that such information is just provided by simple numbers or star ratings. Based on those review types, it is not easy to get specific information that consumers want and use it to make a decision for purchase. Therefore, in this study, we propose a prediction methodology that can provide ratings broken down by evaluation items by performing sentiment analysis on restaurant reviews written in Korean. To this end, we select 'food', 'price', 'service', and 'atmosphere' as the main evaluation items of restaurants, and build a new sentiment dictionary for each evaluation item. It also classifies review sentences by rating item, predicts granular ratings through sentiment analysis, and provides additional information that consumers can use to make decisions. Finally, using MAE and RMSE as evaluation indicators it shows that the rating prediction accuracy of the proposed methodology has been improved than previous studies and presents the use case of proposed methodology.